4.4 Article

Evaluation of Silicon Carbide Nanoparticles as an Additive to Minimize Surfactant Loss during Chemical Flooding

Journal

CHEMISTRYSELECT
Volume 7, Issue 22, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/slct.202104294

Keywords

Adsorption; Silicon carbide; Langmuir; Surfactant; CMC

Funding

  1. Rajiv Gandhi Institute of Petroleum Technology

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Surfactant flooding is an effective method for enhanced oil recovery, but the loss of surfactant due to adsorption and the critical micelle concentration pose challenges. This study demonstrates that the use of Silicon Carbide nanoparticles as additives can reduce surfactant adsorption by 44% and the critical micelle concentration by 14%. The concentration of nanoparticles required is significantly lower than previous literature on other nanoparticles.
Surfactant flooding is a prolific enhanced oil recovery technique. It alters the rock fluid interfacial interactions between reservoir fluids and rocks. However, a pair of impeding factors often limit the economic viability of the process and need to be optimized. The first is the surfactant loss on the adsorbent surface due to adsorption making it a potential environmental problem. The second is the Critical Micelle Concentration (CMC) which governs the amount of surfactant pumped. This study aimed to minimize the surfactant loss and the CMC, thereby optimizing the flooding efficiency and reducing the environmental concerns for nanoparticle applications. To achieve this, Silicon Carbide nanoparticles (SCN) were employed as additives to the surfactant solution. It was observed that the use of SCN reduces the surfactant adsorption by as much as 44 % and the critical micelle concentration by 14 %. The studies also observed that the concentration of SCN required to get these results is dramatically (more than 25 times) lower than previously reported literature on other nanoparticles. To adsorption isotherm studies provide an insight into the type of adsorption taking place. The adsorption isotherm follows the Langmuir model.

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